Daniel Pak

25 papers receiving 414 citations

Peers

Daniel Pak
Comparison fields: 5 of 73
  • Anesthesiology and Pain Medicine 57
  • Orthopedics and Sports Medicine 59
  • Molecular Medicine 28
  • Microbiology 24
  • Pharmacology 56
Replace Ahmet Şen with:
Ahmet Şen Türkiye
John F. Lovejoy United States
Juán Morgaz Spain
Eun Jin Ha South Korea
Florian Stehling Germany
Takahiro Kato Japan
A.M. Korinek France
Russell K. McAllister United States
Elena Caresta Italy
Timon Vassiliou Germany
Daniel Pak relative to Ahmet Şen Türkiye Ahmet Şen's profile →
Citations per field
00.5×11.8×
Ahmet Şen · 1×
Citations per year

Countries citing papers authored by Daniel Pak

Since Specialization
Citations

This map shows the geographic impact of Daniel Pak's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Pak with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Pak more than expected).

Fields of papers citing papers by Daniel Pak

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Pak. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Pak. The network helps show where Daniel Pak may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Pak, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Pak Line = papers co-authored together Daniel Pak links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2018107
2 201276
3 201066
4 202031
5 202025
6 201222
7 202021
8 201119
9 201816
10 201913
11 202212
12 20245
13 20234
14 20252
15 20242
16 20102
17 20241
18 20231
19 20241
20 20211

About Daniel Pak

Daniel Pak is a scholar working on Surgery, Anesthesiology and Pain Medicine, Cardiology and Cardiovascular Medicine, Pulmonary and Respiratory Medicine and Physiology, having authored 31 papers that have together received 432 indexed citations. Recurring topics across this work include Pain Management and Treatment (5 papers), Anesthesia and Pain Management (5 papers), Sarcoma Diagnosis and Treatment (3 papers), Pain Management and Opioid Use (3 papers), Pain Mechanisms and Treatments (3 papers), Head and Neck Cancer Studies (2 papers), Bone Tumor Diagnosis and Treatments (2 papers) and Pericarditis and Cardiac Tamponade (2 papers). The work is most often cited by research in Anesthesiology and Pain Medicine (57 citations), Orthopedics and Sports Medicine (59 citations), Molecular Medicine (28 citations), Microbiology (24 citations) and Pharmacology (56 citations). Daniel Pak has collaborated with scholars based in United States, Netherlands and United Kingdom. Frequent co-authors include Richard D. Urman, Alan D. Kaye, R. Jason Yong, Toby O. Smith, Amitabh Gulati, Adam Brothers, Scott J. Weissman, Heather B. Jaspan, Samuel R. Browd and Angela Campbell. Their work appears in journals such as Regional Anesthesia & Pain Medicine, International Journal of Radiation Oncology*Biology*Physics, Current Pain and Headache Reports, CHEST Journal and Journal of the Pediatric Infectious Diseases Society.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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